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DTSTAMP:20210916T132528Z
LOCATION:Ernesto Bertarelli
DTSTART;TZID=Europe/Stockholm:20210709T140000
DTEND;TZID=Europe/Stockholm:20210709T160000
UID:submissions.pasc-conference.org_PASC21_sess151@linklings.com
SUMMARY:Developing Scientific Codes for Predictive Simulations on Massivel
 y Parallel Heterogeneous Computing Platforms: Integrating Extreme-Scale Co
 mputation, Data Analysis and Visualization II
DESCRIPTION:Minisymposium\n\nMost of the flow solvers, commercial as well 
 as opensource, that are used for turbulent flow simulations are based on s
 patial discretizations that are nominally second-order accurate for evolvi
 ng the compressible and incompressible Navier-Stokes on unstructured meshe
 s that represent the underlying complex geometry. For canonical simulation
 s of incompressible turbulent flows, on the other hand, where the geometry
  of the computational domain is much simpler, the solvers usually make use
  of FFT based pseudo-spectral solvers that could be used in conjunction wi
 th higher-order finite difference schemes. The construction of these solve
 rs for optimal performance on GPU based platforms, and the hardware abstra
 ctions that are used to offload computations to the GPU, is the subject of
  this mini-symposia. Secondly, this mini-symposia will feature a talk that
  assesses the performance of higher-order discretization schemes (with loc
 al support) on GPU based platforms, and their ability to represent the fin
 e scale turbulent flow features when compared with pseudo-spectral solvers
  that have traditionally been used for DNS of canonical flows. Finally, th
 is mini-symposia will also present the simulation of multiphase flows with
  a higher-order lattice Boltzmann method.\n\nPredicting Wakes Behind Build
 ings: A Machine Learning Approach for Extracting Physics Informed Low-Orde
 r Models from Highly Resolved Flow-Field Datasets\n\nFytanidis, Maulik, Ba
 lakrishnan, Kotamarthi\n\nResolved simulations of the atmospheric boundary
  layer (ABL) provide valuable information for optimizing the siting of win
 d turbines in a “distributed wind” scenario, where wind turbin
 es located amidst buildings and vegetation, in an urban/semi-urban/rural s
 etting, supply electricity, di...\n\n---------------------\nIn-Situ Machin
 e Learning for Intelligent Data Capture on Exascale Platforms\n\nDavis\n\n
 We present a framework for developing in-situ anomaly detection algorithms
  with minimal communication and storage requirements, and describe a varie
 ty of spatial and temporal anomaly detection methodologies developed there
 in. We demonstrate the efficacy of these algorithms in the domains of flui
 d dy...\n\n---------------------\nScalable and Parallel Evaluation of Tang
 ent Operators and Gradients in Nonlinear Finite Element Analysis\n\nLazaro
 v\n\nThe goal of this talk is to present, discuss, and demonstrate techniq
 ues, based on automatic differentiation, for finding the Jacobians in nonl
 inear finite element analysis for time-dependent and steady-state applicat
 ions, as well as gradients for PDE constrained optimization problems. The 
 focus is ...\n\n---------------------\nComposing Particle-Based Simulation
  Software for Exascale-Class Machines\n\nSlattery\n\nWithin the Exascale C
 omputing Project (ECP) we have been actively developing libraries for crea
 ting portable and performant particle-based simulations for the expected r
 ange of upcoming exascale-class architectures. This includes application t
 o molecular dynamics (MD) and its derivatives, particle-i...\n\n\nDomain: 
 CS and Math, Emerging Applications, Climate and Weather, Physics, Engineer
 ing
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